AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging
Yetian Chen, Jin Tian

Last modified: 2014-06-21

Abstract


In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs (Tian, He and Ram 2010). We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space to achieve the same quality of approximation. Our algorithm goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery.

Keywords


Bayesian network; Bayesian model averaging; Markov equivalence class; Structure discovery

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